DraftTrace：面向AI辅助写作的多视图分析环境
DraftTrace: A Multi-View Analytics Environment for AI-Integrated Writing
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中文总结 AI 辅助
针对生成式AI影响下写作过程难以评估的问题，提出DraftTrace多视图分析环境，联合捕获产品、过程与AI交互，在81名学生课程中验证其区分性与分析价值。
中文摘要 AI 辅助
生成式人工智能已经改变了学生完成写作作业的方式。最终成果物不再足以理解其产生过程。我们介绍了DraftTrace，一个写作环境，它联合捕获写作的三个互补视图：最终产品、写作过程以及与集成AI助手的交互。DraftTrace重建文档随时间的发展过程，并将这些信号组织为提交、纵向和班级层面的分析，供教师使用。我们在一个包含81名研究生的自然语言处理课程中部署了DraftTrace，并将他们的会话与通过自动化工具输入的LLM生成响应以及复制粘贴输入的响应进行了比较。虽然产品度量可以区分文本表述的差异，但过程度量可以区分文本输入方式的差异。同时考虑这两个视图有助于刻画复制粘贴等情形。交互轨迹显示，学生在写作的不同阶段以不同方式使用助手：在早期阶段用于澄清问题，在后期阶段用于验证答案。一项初步的教师调查强调了多视图写作分析及其可解释性的重要性。
英文摘要
Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which it was produced. We introduce DraftTrace, a writing environment that jointly captures three complementary views of writing: the final product, the writing process and interactions with an integrated AI-assistant. DraftTrace reconstructs how a document develops over time and organizes these signals into submission, longitudinal, and class-level analytics for instructors. We deployed DraftTrace in a graduate NLP course with 81 students and compared their sessions with LLM-generated responses entered by automated tools and with copy-typed responses. While product measures distinguish differences in text formulation, process measures distinguish differences in how text is entered. Considering both views together helps characterize cases such as copy-typing. Interaction traces show that students use the assistant differently across stages of writing: to clarify the question at an early stage and to verify answers at a later stage. A preliminary instructor survey highlights the importance of multi-view writing analytics and their interpretability.
发表机构
- Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
